The Prediction of Stock Market Using Recurrent Neural Network
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2021
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Stock price forecasting is becoming increasingly popular recently in the financial realm. Shares price prediction is important for increasing the interest of speculators in putting money in a company's stock in order to grow the number of shareholders in the stock. Successfully predicting the future price of a stock could result in a sizable return. When it involves forecasting, various methodologies are used. This report uses a replacement stock price prediction framework is proposed utilizing a well-liked model which is Recurrent Neural Network (RNN) model i.e., Long Short-Term Memory (LSTM) model. It is often shown from the simulation results that utilizing these RNN models, i.e., LSTM, and with proper hyper-parameter tuning, the proposed scheme can forecast future stock trend with high accuracy. The RMSE for LSTM model was measured by varying the number of epochs, difference between predicted stock price and actual stock price. The model is trained and evaluated for accuracy with various sizes of knowledge. The assessments are conducted by utilizing a freely accessible dataset for stock markets having date, volume, open, high, low, and closing prices. The major goal of this article is to determine to what degree a Machine Learning algorithm can anticipate the stock market price with greater accuracy.
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Electrical and Computer Engineering
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North South University